The prevalence of social media (e.g., micro-blogging platforms such as Twitter™ and Tumblr™) allows users to share messages and interact with potentially anyone in the world. In general, social media users post blogs about things or activities they did in the past and, more importantly, things or activities they plan to do in the near future. For instance, it has been shown that Twitter™ served an important role in terms of organizing and planning events during the Arab Spring, which is a sequence of protests that swept through the Middle East in early 2011 (see the List of Incorporated Cited Literature References, Literature Reference No. 2).
Traditionally, civil unrest events are predicted by intelligence services which generally need to place their agents in different regions of the world for risk assessment. This could introduce significant delays on the report of the events. Furthermore, it is incredibly difficult (if not impossible) to have human analysts monitor all regions of the world at the same time. The value of civil unrest forecasting has recently attracted attention from research communities across a wide range of disciplines. Several studies have been conducted for this application. For example, in Literature Reference No. 7, a stochastic hybrid dynamical system (S-HDS) model is proposed to generate predictions for social phenomena. In Literature Reference Nos. 8 and 9, effective keyword based approaches are proposed to detect emerging popular events using publicly available Twitter™ data. Gonzalez-Bailon et al. (Literature Reference No. 1) examined the connection between online discussion and the diffusion of protests. Through the analysis of the recruitment patterns of blogging data, connections between online network, social contagion, and collective dynamics were found. In Literature Reference No. 2, the role of modern information communication technologies in the 2011 Egyptian protests was studied. The work showed that the sociopolitical protests were facilitated by social media networks, particularly in regard to their organizational and communication aspects, and social network played an important role in the rapid disintegration of two regimes, Tunisia and Egypt. Chen et al. (see Literature Reference No. 3) took the next step to propose an approach based on spatial surrogates modeling to forecast social mobilization and civil unrests. Specifically, they define a dictionary of key terms related to protests and apply a statistical model to identify future civil unrest regions and their potential magnitudes.
In a subsequent work (see Literature Reference No. 4), a semi-supervised system was proposed to help users automatically detect and interactively visualize events of a targeted type from Twitter™, such as crimes, civil unrests, and disease outbreaks. Their model first applied transfer learning and label propagation to automatically generate labeled data, then learned a customized text classifier based on mini-clustering. Finally, their model applied fast spatial scan statistics to estimate the locations of events. A similar work was performed by Compton et al. (see Literature Reference No. 5) for civil unrest forecasting using Twitter™ data using domain keywords.
Thus, a continuing need exists for an automatic civil unrest detection system that mines social media data comprising blog posts using simple domain keywords.